Nonlinear Processes in Geophysics
Nonlinear Processes in Geophysics
NPG
Articles | Volume 28, issue 3
Articles | Volume 28, issue 3
https://doi.org/10.5194/npg-28-423-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-28-423-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 28, issue 3
Research article
 | 
10 Sep 2021
Research article |  | 10 Sep 2021

Enhancing geophysical flow machine learning performance via scale separation

Davide Faranda, Mathieu Vrac, Pascal Yiou, Flavio Maria Emanuele Pons, Adnane Hamid, Giulia Carella, Cedric Ngoungue Langue, Soulivanh Thao, and Valerie Gautard

Related authors

Analysing 23 years of warm-season derechos in France: a climatology and investigation of synoptic and environmental changes
Lucas Fery and Davide Faranda
Weather Clim. Dynam., 5, 439–461, https://doi.org/10.5194/wcd-5-439-2024,https://doi.org/10.5194/wcd-5-439-2024, 2024
Short summary
ClimaMeter: Contextualising Extreme Weather in a Changing Climate
Davide Faranda, Gabriele Messori, Erika Coppola, Tommaso Alberti, Mathieu Vrac, Flavio Pons, Pascal Yiou, Marion Saint Lu, Andreia N. S. Hisi, Patrick Brockmann, Stavros Dafis, and Robert Vautard
EGUsphere, https://doi.org/10.5194/egusphere-2023-2643,https://doi.org/10.5194/egusphere-2023-2643, 2023
Short summary
The link between European warm-temperature extremes and atmospheric persistence
Emma Holmberg, Gabriele Messori, Rodrigo Caballero, and Davide Faranda
Earth Syst. Dynam., 14, 737–765, https://doi.org/10.5194/esd-14-737-2023,https://doi.org/10.5194/esd-14-737-2023, 2023
Short summary
A climate-change attribution retrospective of some impactful weather extremes of 2021
Davide Faranda, Stella Bourdin, Mireia Ginesta, Meriem Krouma, Robin Noyelle, Flavio Pons, Pascal Yiou, and Gabriele Messori
Weather Clim. Dynam., 3, 1311–1340, https://doi.org/10.5194/wcd-3-1311-2022,https://doi.org/10.5194/wcd-3-1311-2022, 2022
Short summary
Statistical reconstruction of European winter snowfall in reanalysis and climate models based on air temperature and total precipitation
Flavio Maria Emanuele Pons and Davide Faranda
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 155–186, https://doi.org/10.5194/ascmo-8-155-2022,https://doi.org/10.5194/ascmo-8-155-2022, 2022
Short summary
More articles (12)

Related subject area

Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Big data and artificial intelligence
Robust weather-adaptive post-processing using model output statistics random forests
Thomas Muschinski, Georg J. Mayr, Achim Zeileis, and Thorsten Simon
Nonlin. Processes Geophys., 30, 503–514, https://doi.org/10.5194/npg-30-503-2023,https://doi.org/10.5194/npg-30-503-2023, 2023
Short summary
Guidance on how to improve vertical covariance localization based on a 1000-member ensemble
Tobias Necker, David Hinger, Philipp Johannes Griewank, Takemasa Miyoshi, and Martin Weissmann
Nonlin. Processes Geophys., 30, 13–29, https://doi.org/10.5194/npg-30-13-2023,https://doi.org/10.5194/npg-30-13-2023, 2023
Short summary
Weather pattern dynamics over western Europe under climate change: predictability, information entropy and production
Stéphane Vannitsem
Nonlin. Processes Geophys., 30, 1–12, https://doi.org/10.5194/npg-30-1-2023,https://doi.org/10.5194/npg-30-1-2023, 2023
Short summary
Calibrated ensemble forecasts of the height of new snow using quantile regression forests and ensemble model output statistics
Guillaume Evin, Matthieu Lafaysse, Maxime Taillardat, and Michaël Zamo
Nonlin. Processes Geophys., 28, 467–480, https://doi.org/10.5194/npg-28-467-2021,https://doi.org/10.5194/npg-28-467-2021, 2021
Short summary
Training a convolutional neural network to conserve mass in data assimilation
Yvonne Ruckstuhl, Tijana Janjić, and Stephan Rasp
Nonlin. Processes Geophys., 28, 111–119, https://doi.org/10.5194/npg-28-111-2021,https://doi.org/10.5194/npg-28-111-2021, 2021
Short summary
More articles (2)

Cited articles

Bassett, D. and Sporns, O.: Network neuroscience, Nat. Neurosci., 20, 353–364, https://doi.org/10.1038/nn.4502, 2017. a
Berkson, J.: Minimum Chi-Square, not Maximum Likelihood!, Ann. Statist., 8, 457–487, https://doi.org/10.1214/aos/1176345003, 1980. a
Bolton, T. and Zanna, L.: Applications of deep learning to ocean data inference and subgrid parameterization, J. Adv. Model. Earth Sy., 11, 376–399, 2019. a
Bonferroni, C.: Teoria statistica delle classi e calcolo delle probabilita, Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commericiali di Firenze, 8, 3–62, 1936. a
Bony, S., Stevens, B., Frierson, D. M. W., Jakob, C., Kageyama, M., Pincus, R., Shepherd, T. G., Sherwood, S. C., Siebesma, A. P., Sobel, A. H., Watanabe, M., and Webb, M. J.: Clouds, circulation and climate sensitivity, Nat. Geosci., 8, 261–268, https://doi.org/10.1038/ngeo2398, 2015. a
More articles (72)
Download
Short summary
Machine learning approaches are spreading rapidly in climate sciences. They are of great help in many practical situations where using the underlying equations is difficult because of the limitation in computational power. Here we use a systematic approach to investigate the limitations of the popular echo state network algorithms used to forecast the long-term behaviour of chaotic systems, such as the weather. Our results show that noise and intermittency greatly affect the performances.
Read more